apply change and fixed test cases

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -8,16 +8,16 @@ import json
 from typing import AsyncGenerator
 
 from llama_models.datatypes import CoreModelId, SamplingStrategy
-
 from llama_models.llama3.api.chat_format import ChatFormat
-
-from llama_models.llama3.api.datatypes import Message
 from llama_models.llama3.api.tokenizer import Tokenizer
-
 from openai import OpenAI
 
+from llama_stack.apis.common.content_types import (
+    ImageContentItem,
+    InterleavedContent,
+    TextContentItem,
+)
 from llama_stack.apis.inference import *  # noqa: F403
-
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     ModelRegistryHelper,
@@ -25,9 +25,8 @@ from llama_stack.providers.utils.inference.model_registry import (
 from llama_stack.providers.utils.inference.openai_compat import (
     process_chat_completion_stream_response,
 )
-
 from llama_stack.providers.utils.inference.prompt_adapter import (
-    convert_image_media_to_url,
+    convert_image_content_to_url,
 )
 
 from .config import SambaNovaImplConfig
@@ -86,7 +85,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
     async def completion(
         self,
         model_id: str,
-        content: InterleavedTextMedia,
+        content: InterleavedContent,
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
@@ -129,6 +128,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
         self, request: ChatCompletionRequest
     ) -> ChatCompletionResponse:
         response = self._get_client().chat.completions.create(**request)
+
         choice = response.choices[0]
 
         result = ChatCompletionResponse(
@@ -163,7 +163,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
     async def embeddings(
         self,
         model_id: str,
-        contents: List[InterleavedTextMedia],
+        contents: List[InterleavedContent],
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
 
@@ -244,21 +244,19 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
 
     async def convert_to_sambanova_content(self, message: Message) -> dict:
         async def _convert_content(content) -> dict:
-            if isinstance(content, ImageMedia):
+            if isinstance(content, ImageContentItem):
-                download = False
+                url = await convert_image_content_to_url(content, download=True)
-                if isinstance(content, ImageMedia) and isinstance(content.image, URL):
+                # A fix to make sure the call sucess.
-                    download = content.image.uri.startswith("https://")
+                components = url.split(";base64")
+                url = f"{components[0].lower()};base64{components[1]}"
                 return {
                     "type": "image_url",
-                    "image_url": {
+                    "image_url": {"url": url},
-                        "url": await convert_image_media_to_url(
-                            content, download=download
-                        ),
-                    },
                 }
             else:
-                assert isinstance(content, str)
+                text = content.text if isinstance(content, TextContentItem) else content
-                return {"type": "text", "text": content}
+                assert isinstance(text, str)
+                return {"type": "text", "text": text}
 
         if isinstance(message.content, list):
             # If it is a list, the text content should be wrapped in dict
@@ -320,11 +318,14 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
         if not tool_calls:
             return []
 
+        for call in tool_calls:
+            call_function_arguments = json.loads(call.function.arguments)
+
         compitable_tool_calls = [
             ToolCall(
                 call_id=call.id,
                 tool_name=call.function.name,
-                arguments=call.function.arguments,
+                arguments=call_function_arguments,
             )
             for call in tool_calls
         ]

llama_stack/providers/tests/inference/test_embeddings.py

@@ -6,7 +6,8 @@
 
 import pytest
 
-from llama_stack.apis.inference import EmbeddingsResponse, ModelType
+from llama_stack.apis.inference import EmbeddingsResponse
+from llama_stack.apis.models import ModelType
 
 # How to run this test:
 # pytest -v -s llama_stack/providers/tests/inference/test_embeddings.py

llama_stack/providers/tests/inference/test_model_registration.py

@@ -59,7 +59,7 @@ class TestModelRegistration:
             },
         )
 
-        with pytest.raises(AssertionError) as exc_info:
+        with pytest.raises(ValueError) as exc_info:
             await models_impl.register_model(
                 model_id="custom-model-2",
                 metadata={

llama_stack/providers/tests/inference/test_text_inference.py

@@ -383,6 +383,12 @@ class TestInference:
             # TODO(aidand): Remove this skip once Groq's tool calling for Llama3.2 works better
             pytest.skip("Groq's tool calling for Llama3.2 doesn't work very well")
 
+        if provider.__provider_spec__.provider_type == "remote::sambanova" and (
+            "-1B-" in inference_model or "-3B-" in inference_model
+        ):
+            # TODO(snova-edawrdm): Remove this skip once SambaNova's tool calling for 1B/ 3B
+            pytest.skip("Sambanova's tool calling for lightweight models don't work")
+
         messages = sample_messages + [
             UserMessage(
                 content="What's the weather like in San Francisco?",
@@ -429,6 +435,9 @@ class TestInference:
         ):
             # TODO(aidand): Remove this skip once Groq's tool calling for Llama3.2 works better
             pytest.skip("Groq's tool calling for Llama3.2 doesn't work very well")
+        if provider.__provider_spec__.provider_type == "remote::sambanova":
+            # TODO(snova-edawrdm): Remove this skip once SambaNova's tool calling under streaming is supported (we are working on it)
+            pytest.skip("Sambanova's tool calling for streaming doesn't work")
 
         messages = sample_messages + [
             UserMessage(

llama_stack/providers/tests/inference/test_vision_inference.py

@@ -145,7 +145,7 @@ class TestVisionModelInference:
             assert len(grouped[ChatCompletionResponseEventType.complete]) == 1
 
             content = "".join(
-                chunk.event.delta
+                chunk.event.delta.text
                 for chunk in grouped[ChatCompletionResponseEventType.progress]
             )
             for expected_string in expected_strings: